(This article was originally published at Learn and Teach Statistics and Operations Research, and syndicated at StatsBlogs.)
The following is a guest post by Tony Hak of Rotterdam School of Management. I know Tony would love some discussion about it in the comments. I remain undecided either way, so would like to hear arguments.
GOOD REASONS FOR NOT TEACHING SIGNIFICANCE TESTING
It is now well understood that p-values are not informative and are not replicable. Soon null hypothesis significance testing (NHST) will be obsolete and will be replaced by the so-called “new” statistics (estimation and meta-analysis). This requires that undergraduate courses in statistics now already must teach estimation and meta-analysis as the preferred way to present and analyze empirical results. If not, then the statistical skills of the graduates from these courses will be outdated on the day these graduates leave school. But it is less evident whether or not NHST (though not preferred as an analytic tool) should still be taught. Because estimation is already routinely taught as a preparation for the teaching of NHST, the necessary reform in teaching will not require the addition of new elements in current programs but rather the removal of the current emphasis on NHST or the complete removal of the teaching of NHST from the curriculum. The current trend is to continue the teaching of NHST. In my view, however, teaching of NHST should be discontinued immediately because it is (1) ineffective and (2) dangerous, and (3) it serves no aim.
1. Ineffective: NHST is difficult to understand and it is very hard to teach it successfully
We know that even good researchers often do not appreciate the fact that NHST outcomes are subject to sampling variation and believe that a “significant” result obtained in one study almost guarantees a significant result in a replication, even one with a smaller sample size. Is it then surprising that also our students do not understand what NHST outcomes do tell us and what they do not tell us? In fact, statistics teachers know that the principles and procedures of NHST are not well understood by undergraduate students who have successfully passed their courses on NHST. Courses on NHST fail to achieve their self-stated objectives, assuming that these objectives include achieving a correct understanding of the aims, assumptions, and procedures of NHST as well as a proper interpretation of its outcomes. It is very hard indeed to find a comment on NHST in any student paper (an essay, a thesis) that is close to a correct characterization of NHST or its outcomes. There are many reasons for this failure, but obviously the most important one is that NHST a very complicated and counterintuitive procedure. It requires students and researchers to understand that a p-value is attached to an outcome (an estimate) based on its location in (or relative to) an imaginary distribution of sample outcomes around the null. Another reason, connected to their failure to understand what NHST is and does, is that students believe that NHST “corrects for chance” and hence they cannot cognitively accept that p-values themselves are subject to sampling variation (i.e. chance)
2. Dangerous: NHST thinking is addictive
One might argue that there is no harm in adding a p-value to an estimate in a research report and, hence, that there is no harm in teaching NHST, additionally to teaching estimation. However, the mixed experience with statistics reform in clinical and epidemiological research suggests that a more radical change is needed. Reports of clinical trials and of studies in clinical epidemiology now usually report estimates and confidence intervals, in addition to p-values. However, as Fidler et al. (2004) have shown, and contrary to what one would expect, authors continue to discuss their results in terms of significance. Fidler et al. therefore concluded that “editors can lead researchers to confidence intervals, but can’t make them think”. This suggests that a successful statistics reform requires a cognitive change that should be reflected in how results are interpreted in the Discussion sections of published reports.
The stickiness of dichotomous thinking can also be illustrated with the results of a more recent study of Coulson et al. (2010). They presented estimates and confidence intervals obtained in two studies to a group of researchers in psychology and medicine, and asked them to compare the results of the two studies and to interpret the difference between them. It appeared that a considerable proportion of these researchers, first, used the information about the confidence intervals to make a decision about the significance of the results (in one study) or the non-significance of the results (of the other study) and, then, drew the incorrect conclusion that the results of the two studies were in conflict. Note that no NHST information was provided and that participants were not asked in any way to “test” or to use dichotomous thinking. The results of this study suggest that NHST thinking can (and often will) be used by those who are familiar with it.
The fact that it appears to be very difficult for researchers to break the habit of thinking in terms of “testing” is, as with every addiction, a good reason for avoiding that future researchers come into contact with it in the first place and, if contact cannot be avoided, for providing them with robust resistance mechanisms. The implication for statistics teaching is that students should, first, learn estimation as the preferred way of presenting and analyzing research information and that they get introduced to NHST, if at all, only after estimation has become their routine statistical practice.
3. It serves no aim: Relevant information can be found in research reports anyway
Our experience that teaching of NHST fails its own aims consistently (because NHST is too difficult to understand) and the fact that NHST appears to be dangerous and addictive are two good reasons to immediately stop teaching NHST. But there is a seemingly strong argument for continuing to introduce students to NHST, namely that a new generation of graduates will not be able to read the (past and current) academic literature in which authors themselves routinely focus on the statistical significance of their results. It is suggested that someone who does not know NHST cannot correctly interpret outcomes of NHST practices. This argument has no value for the simple reason that it is assumed in the argument that NHST outcomes are relevant and should be interpreted. But the reason that we have the current discussion about teaching is the fact that NHST outcomes are at best uninformative (beyond the information already provided by estimation) and are at worst misleading or plain wrong. The point is all along that nothing is lost by just ignoring the information that is related to NHST in a research report and by focusing only on the information that is provided about the observed effect size and its confidence interval.
Bibliography
Coulson, M., Healy, M., Fidler, F., & Cumming, G. (2010). Confidence Intervals Permit, But Do Not Guarantee, Better Inference than Statistical Significance Testing. Frontiers in Quantitative Psychology and Measurement, 20(1), 37-46.
Fidler, F., Thomason, N., Finch, S., & Leeman, J. (2004). Editors Can Lead Researchers to Confidence Intervals, But Can’t Make Them Think. Statistical Reform Lessons from Medicine. Psychological Science, 15(2): 119-126.
This text is a condensed version of the paper “After Statistics Reform: Should We Still Teach Significance Testing?” published in the Proceedings of ICOTS9.
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